Overview

Dataset statistics

Number of variables10
Number of observations1246
Missing cells1122
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.5 KiB
Average record size in memory80.1 B

Variable types

DateTime1
Numeric9

Alerts

Coker % is highly overall correlated with coker M3/hrHigh correlation
Heater outlet °C is highly overall correlated with Delta Temp 02-H-01 °C and 2 other fieldsHigh correlation
Heater inlet °C is highly overall correlated with Delta Temp 02-H-01 °C and 3 other fieldsHigh correlation
Delta Temp 02-H-01 °C is highly overall correlated with Heater outlet °C and 4 other fieldsHigh correlation
coker M3/hr is highly overall correlated with Coker %High correlation
Aver Skin °C is highly overall correlated with Heater outlet °C and 4 other fieldsHigh correlation
Max Skin °C is highly overall correlated with Heater outlet °C and 4 other fieldsHigh correlation
02-H-01 Duty (Gcal/hr) is highly overall correlated with Heater inlet °C and 3 other fieldsHigh correlation
Naphtha Feed M3/hr has 124 (10.0%) missing valuesMissing
Coker % has 124 (10.0%) missing valuesMissing
Heater outlet °C has 123 (9.9%) missing valuesMissing
Heater inlet °C has 123 (9.9%) missing valuesMissing
Delta Temp 02-H-01 °C has 123 (9.9%) missing valuesMissing
coker M3/hr has 130 (10.4%) missing valuesMissing
Aver Skin °C has 124 (10.0%) missing valuesMissing
Max Skin °C has 124 (10.0%) missing valuesMissing
02-H-01 Duty (Gcal/hr) has 127 (10.2%) missing valuesMissing
Date has unique valuesUnique
Coker % has 138 (11.1%) zerosZeros
coker M3/hr has 132 (10.6%) zerosZeros

Reproduction

Analysis started2023-07-07 11:35:47.734372
Analysis finished2023-07-07 11:36:05.888577
Duration18.15 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct1246
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
Minimum2020-01-01 00:00:00
Maximum2023-05-31 00:00:00
2023-07-07T14:36:06.014525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:06.188218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Naphtha Feed M3/hr
Real number (ℝ)

MISSING 

Distinct1122
Distinct (%)100.0%
Missing124
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean196.91606
Minimum102.58134
Maximum221.25022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:06.353153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum102.58134
5-th percentile162.21738
Q1185.49705
median200.34316
Q3211.41798
95-th percentile218.64241
Maximum221.25022
Range118.66888
Interquartile range (IQR)25.920927

Descriptive statistics

Standard deviation19.404622
Coefficient of variation (CV)0.098542606
Kurtosis2.6911561
Mean196.91606
Median Absolute Deviation (MAD)12.205264
Skewness-1.4326987
Sum220939.82
Variance376.53934
MonotonicityNot monotonic
2023-07-07T14:36:06.628452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194.977843 1
 
0.1%
194.8569679 1
 
0.1%
197.4557476 1
 
0.1%
195.4906546 1
 
0.1%
194.699995 1
 
0.1%
195.0368493 1
 
0.1%
193.046505 1
 
0.1%
205.5436662 1
 
0.1%
195.0041739 1
 
0.1%
209.7516448 1
 
0.1%
Other values (1112) 1112
89.2%
(Missing) 124
 
10.0%
ValueCountFrequency (%)
102.5813382 1
0.1%
105.8245433 1
0.1%
107.8125459 1
0.1%
110.51264 1
0.1%
113.6966464 1
0.1%
116.5846861 1
0.1%
119.7912031 1
0.1%
119.9166514 1
0.1%
120.0822786 1
0.1%
125.7316119 1
0.1%
ValueCountFrequency (%)
221.2502187 1
0.1%
221.245978 1
0.1%
221.0718149 1
0.1%
220.9243247 1
0.1%
220.9069214 1
0.1%
220.8178101 1
0.1%
220.7177951 1
0.1%
220.7037462 1
0.1%
220.6718152 1
0.1%
220.6049976 1
0.1%

Coker %
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct985
Distinct (%)87.8%
Missing124
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.080028751
Minimum0
Maximum0.20252967
Zeros138
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:06.823211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.075491922
median0.09046799
Q30.10048929
95-th percentile0.11970148
Maximum0.20252967
Range0.20252967
Interquartile range (IQR)0.024997365

Descriptive statistics

Standard deviation0.036202455
Coefficient of variation (CV)0.45236811
Kurtosis0.71301435
Mean0.080028751
Median Absolute Deviation (MAD)0.012055265
Skewness-1.1214058
Sum89.792258
Variance0.0013106177
MonotonicityNot monotonic
2023-07-07T14:36:07.024893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138
 
11.1%
0.1091794459 1
 
0.1%
0.09469474936 1
 
0.1%
0.1060053479 1
 
0.1%
0.1074538132 1
 
0.1%
0.1098813787 1
 
0.1%
0.1065951511 1
 
0.1%
0.1085350406 1
 
0.1%
0.1072854287 1
 
0.1%
0.1050506767 1
 
0.1%
Other values (975) 975
78.3%
(Missing) 124
 
10.0%
ValueCountFrequency (%)
0 138
11.1%
0.006925884316 1
 
0.1%
0.008347113152 1
 
0.1%
0.008976007539 1
 
0.1%
0.01061399029 1
 
0.1%
0.01862659427 1
 
0.1%
0.02050669325 1
 
0.1%
0.02139064521 1
 
0.1%
0.02236352956 1
 
0.1%
0.02423449769 1
 
0.1%
ValueCountFrequency (%)
0.2025296669 1
0.1%
0.188287557 1
0.1%
0.1855223591 1
0.1%
0.160349303 1
0.1%
0.1566503061 1
0.1%
0.1562757939 1
0.1%
0.1557077744 1
0.1%
0.1511671501 1
0.1%
0.1438217346 1
0.1%
0.1382243723 1
0.1%

Heater outlet °C
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1123
Distinct (%)100.0%
Missing123
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean315.85937
Minimum268.6019
Maximum324.94784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:07.198198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum268.6019
5-th percentile312.46705
Q1314.6295
median315.11394
Q3317.72435
95-th percentile320.06765
Maximum324.94784
Range56.345941
Interquartile range (IQR)3.0948474

Descriptive statistics

Standard deviation3.3089399
Coefficient of variation (CV)0.010475991
Kurtosis79.534257
Mean315.85937
Median Absolute Deviation (MAD)1.5470136
Skewness-5.8978724
Sum354710.07
Variance10.949083
MonotonicityNot monotonic
2023-07-07T14:36:07.348186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
314.9844844 1
 
0.1%
313.6916351 1
 
0.1%
314.4724388 1
 
0.1%
314.1190707 1
 
0.1%
314.9017843 1
 
0.1%
314.9285889 1
 
0.1%
314.7570928 1
 
0.1%
314.6896935 1
 
0.1%
314.9175669 1
 
0.1%
315.0749385 1
 
0.1%
Other values (1113) 1113
89.3%
(Missing) 123
 
9.9%
ValueCountFrequency (%)
268.6019033 1
0.1%
268.6047058 1
0.1%
285.2540194 1
0.1%
300.7039388 1
0.1%
305.2511609 1
0.1%
305.5504964 1
0.1%
308.1494713 1
0.1%
308.4538091 1
0.1%
308.7608134 1
0.1%
309.508316 1
0.1%
ValueCountFrequency (%)
324.9478442 1
0.1%
324.3824755 1
0.1%
323.2100665 1
0.1%
322.4173063 1
0.1%
322.3168475 1
0.1%
322.3155238 1
0.1%
321.8990491 1
0.1%
321.4428533 1
0.1%
321.3763008 1
0.1%
321.3109665 1
0.1%

Heater inlet °C
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1105
Distinct (%)98.4%
Missing123
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean289.37341
Minimum27.978311
Maximum302.20266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:07.510579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum27.978311
5-th percentile280.46502
Q1285.83733
median288.65107
Q3294.73282
95-th percentile299.32364
Maximum302.20266
Range274.22435
Interquartile range (IQR)8.8954875

Descriptive statistics

Standard deviation11.390509
Coefficient of variation (CV)0.039362668
Kurtosis319.26413
Mean289.37341
Median Absolute Deviation (MAD)3.4927559
Skewness-15.12775
Sum324966.34
Variance129.7437
MonotonicityNot monotonic
2023-07-07T14:36:07.653330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285.9253324 2
 
0.2%
284.6026649 2
 
0.2%
285.6426646 2
 
0.2%
285.5946655 2
 
0.2%
285.2533353 2
 
0.2%
286.6079992 2
 
0.2%
285.7866656 2
 
0.2%
285.8079999 2
 
0.2%
300.3573278 2
 
0.2%
288.9280014 2
 
0.2%
Other values (1095) 1103
88.5%
(Missing) 123
 
9.9%
ValueCountFrequency (%)
27.97831106 1
0.1%
96.66666714 1
0.1%
246.869332 1
0.1%
270.0144024 1
0.1%
271.5751998 1
0.1%
273.4284007 1
0.1%
274.1387545 1
0.1%
274.404665 1
0.1%
274.5077769 1
0.1%
274.5443548 1
0.1%
ValueCountFrequency (%)
302.2026647 1
0.1%
301.9250234 1
0.1%
301.7226677 1
0.1%
301.6959941 1
0.1%
301.5893339 1
0.1%
301.3546651 1
0.1%
301.3013318 1
0.1%
301.2853317 1
0.1%
301.2426631 1
0.1%
301.088 1
0.1%

Delta Temp 02-H-01 °C
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1123
Distinct (%)100.0%
Missing123
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean26.485961
Minimum9.6478831
Maximum240.62639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:07.812611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9.6478831
5-th percentile14.018773
Q120.157035
median27.751935
Q331.362064
95-th percentile35.788704
Maximum240.62639
Range230.97851
Interquartile range (IQR)11.20503

Descriptive statistics

Standard deviation10.368544
Coefficient of variation (CV)0.39147321
Kurtosis195.17243
Mean26.485961
Median Absolute Deviation (MAD)4.5841382
Skewness10.169284
Sum29743.734
Variance107.50671
MonotonicityNot monotonic
2023-07-07T14:36:08.028381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.28599548 1
 
0.1%
19.06345749 1
 
0.1%
18.88804118 1
 
0.1%
18.82977931 1
 
0.1%
19.34350332 1
 
0.1%
18.77885437 1
 
0.1%
19.26784643 1
 
0.1%
17.82187271 1
 
0.1%
18.77952194 1
 
0.1%
19.38800303 1
 
0.1%
Other values (1113) 1113
89.3%
(Missing) 123
 
9.9%
ValueCountFrequency (%)
9.647883097 1
0.1%
10.65814082 1
0.1%
10.8584048 1
0.1%
11.10512034 1
0.1%
11.17646662 1
0.1%
11.22351456 1
0.1%
11.3031133 1
0.1%
11.34317144 1
0.1%
11.3944931 1
0.1%
11.39674759 1
0.1%
ValueCountFrequency (%)
240.6263947 1
0.1%
171.9352361 1
0.1%
40.93612989 1
0.1%
40.74752045 1
0.1%
40.53309631 1
0.1%
40.38252258 1
0.1%
40.32682419 1
0.1%
40.12545649 1
0.1%
39.62033844 1
0.1%
39.60159429 1
0.1%

coker M3/hr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct985
Distinct (%)88.3%
Missing130
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean15.904112
Minimum0
Maximum23.199331
Zeros132
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:08.384827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.672391
median18.64513
Q320.19257
95-th percentile21.838963
Maximum23.199331
Range23.199331
Interquartile range (IQR)4.520179

Descriptive statistics

Standard deviation6.8721667
Coefficient of variation (CV)0.4321
Kurtosis0.82670561
Mean15.904112
Median Absolute Deviation (MAD)1.9281615
Skewness-1.4986132
Sum17748.989
Variance47.226675
MonotonicityNot monotonic
2023-07-07T14:36:08.723485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
10.6%
23.0494802 1
 
0.1%
20.3913093 1
 
0.1%
20.46396192 1
 
0.1%
20.95745317 1
 
0.1%
21.39390389 1
 
0.1%
20.83835586 1
 
0.1%
21.43086759 1
 
0.1%
20.90531333 1
 
0.1%
20.48425341 1
 
0.1%
Other values (975) 975
78.3%
(Missing) 130
 
10.4%
ValueCountFrequency (%)
0 132
10.6%
1.441639718 1
 
0.1%
1.587288941 1
 
0.1%
1.75849863 1
 
0.1%
2.112850722 1
 
0.1%
2.331542252 1
 
0.1%
2.966341626 1
 
0.1%
3.219335746 1
 
0.1%
3.70414879 1
 
0.1%
4.089114899 1
 
0.1%
ValueCountFrequency (%)
23.19933128 1
0.1%
23.15484699 1
0.1%
23.14566247 1
0.1%
23.08192555 1
0.1%
23.0494802 1
0.1%
22.98809743 1
0.1%
22.9459734 1
0.1%
22.93949246 1
0.1%
22.91088597 1
0.1%
22.85524933 1
0.1%

Aver Skin °C
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1122
Distinct (%)100.0%
Missing124
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean463.63483
Minimum175.95283
Maximum522.97736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:09.068508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum175.95283
5-th percentile401.44782
Q1433.69767
median473.08101
Q3493.55963
95-th percentile510.01186
Maximum522.97736
Range347.02453
Interquartile range (IQR)59.861959

Descriptive statistics

Standard deviation36.258079
Coefficient of variation (CV)0.078203959
Kurtosis2.3727923
Mean463.63483
Median Absolute Deviation (MAD)26.529756
Skewness-0.84588149
Sum520198.28
Variance1314.6483
MonotonicityNot monotonic
2023-07-07T14:36:09.403382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437.3922103 1
 
0.1%
435.8179134 1
 
0.1%
438.7090051 1
 
0.1%
440.4896779 1
 
0.1%
439.3538231 1
 
0.1%
443.3157118 1
 
0.1%
440.4607485 1
 
0.1%
449.6926845 1
 
0.1%
431.3770894 1
 
0.1%
456.0738961 1
 
0.1%
Other values (1112) 1112
89.2%
(Missing) 124
 
10.0%
ValueCountFrequency (%)
175.9528332 1
0.1%
358.3604151 1
0.1%
382.4626182 1
0.1%
383.391856 1
0.1%
385.3333285 1
0.1%
385.3508992 1
0.1%
385.4526361 1
0.1%
386.8756669 1
0.1%
387.4379171 1
0.1%
387.5794734 1
0.1%
ValueCountFrequency (%)
522.9773613 1
0.1%
522.5519739 1
0.1%
521.6571393 1
0.1%
517.9431092 1
0.1%
517.8217784 1
0.1%
516.1491111 1
0.1%
515.7991945 1
0.1%
514.7682225 1
0.1%
514.7310286 1
0.1%
514.5154443 1
0.1%

Max Skin °C
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1118
Distinct (%)99.6%
Missing124
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean519.86284
Minimum213.89333
Maximum588.99534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:09.763093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum213.89333
5-th percentile449.81547
Q1486.79605
median528.70603
Q3555.7565
95-th percentile569.56755
Maximum588.99534
Range375.102
Interquartile range (IQR)68.960453

Descriptive statistics

Standard deviation41.471886
Coefficient of variation (CV)0.07977467
Kurtosis1.6615967
Mean519.86284
Median Absolute Deviation (MAD)30.540216
Skewness-0.85782033
Sum583286.1
Variance1719.9173
MonotonicityNot monotonic
2023-07-07T14:36:10.123138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
562.258667 2
 
0.2%
570.5786641 2
 
0.2%
565.4046631 2
 
0.2%
565.2313334 2
 
0.2%
550.4199956 1
 
0.1%
468.7951673 1
 
0.1%
473.5833549 1
 
0.1%
505.3329697 1
 
0.1%
548.800855 1
 
0.1%
551.2864863 1
 
0.1%
Other values (1108) 1108
88.9%
(Missing) 124
 
10.0%
ValueCountFrequency (%)
213.893334 1
0.1%
416.9100037 1
0.1%
417.620669 1
0.1%
418.5913302 1
0.1%
418.790671 1
0.1%
418.8079974 1
0.1%
418.8600006 1
0.1%
419.7613322 1
0.1%
420.0819995 1
0.1%
421.8586667 1
0.1%
ValueCountFrequency (%)
588.9953384 1
0.1%
586.967336 1
0.1%
583.5786616 1
0.1%
582.1792857 1
0.1%
581.4813385 1
0.1%
580.3373413 1
0.1%
580.3373362 1
0.1%
578.8380025 1
0.1%
578.4133377 1
0.1%
578.2746633 1
0.1%

02-H-01 Duty (Gcal/hr)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1119
Distinct (%)100.0%
Missing127
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean3.5116429
Minimum1.7491729
Maximum6.2086941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-07-07T14:36:10.518498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.7491729
5-th percentile2.1896198
Q12.9564271
median3.5479052
Q34.0710536
95-th percentile4.6835146
Maximum6.2086941
Range4.4595211
Interquartile range (IQR)1.1146264

Descriptive statistics

Standard deviation0.77694512
Coefficient of variation (CV)0.22124833
Kurtosis-0.59809484
Mean3.5116429
Median Absolute Deviation (MAD)0.56531043
Skewness-0.13881639
Sum3929.5284
Variance0.60364373
MonotonicityNot monotonic
2023-07-07T14:36:10.908997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.247972759 1
 
0.1%
2.445229574 1
 
0.1%
2.448056853 1
 
0.1%
2.426679855 1
 
0.1%
2.465715868 1
 
0.1%
2.525269008 1
 
0.1%
2.598087087 1
 
0.1%
2.41741521 1
 
0.1%
2.372096573 1
 
0.1%
2.77624046 1
 
0.1%
Other values (1109) 1109
89.0%
(Missing) 127
 
10.2%
ValueCountFrequency (%)
1.749172932 1
0.1%
1.789157497 1
0.1%
1.800075998 1
0.1%
1.800646796 1
0.1%
1.80870357 1
0.1%
1.853075828 1
0.1%
1.86566406 1
0.1%
1.891599112 1
0.1%
1.904429425 1
0.1%
1.910564687 1
0.1%
ValueCountFrequency (%)
6.208694062 1
0.1%
5.504663652 1
0.1%
5.260843131 1
0.1%
5.256193836 1
0.1%
5.224913139 1
0.1%
5.198503958 1
0.1%
5.173342179 1
0.1%
5.137268704 1
0.1%
5.113622232 1
0.1%
5.107817665 1
0.1%

Interactions

2023-07-07T14:36:02.160703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:48.092544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.427772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:51.821970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:53.883417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:55.130325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:57.488610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.413888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:00.727506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:02.341277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:48.293402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.576045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:52.121405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.027438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:55.281296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:57.773474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.561264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:00.875003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:02.645937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:48.449496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.735979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:52.447833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.172871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:55.448232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:58.085124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.712559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:01.143511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:02.922113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:48.583091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.989976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:52.721410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.308235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:55.725340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:58.354931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.857129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:01.282239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:03.181595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:48.719833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:50.278200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:53.006435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.442055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:56.000469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:58.615822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.985986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:01.424678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:03.512868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:48.868733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:50.604786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:53.232734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.576252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:56.273246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:58.890090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:00.152935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:01.580885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:03.768424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.000435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:50.895295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:53.366722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.703073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:56.553977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.010443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:00.285390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:01.717085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:04.049145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.137066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:51.201573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:53.507350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.840787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:56.871990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.142264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:00.432911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:01.860133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:04.361459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:49.291920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:51.534344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:53.656811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:54.986362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:57.199238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:35:59.289634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:00.589405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-07-07T14:36:02.018882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-07-07T14:36:11.448272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Naphtha Feed M3/hrCoker %Heater outlet °CHeater inlet °CDelta Temp 02-H-01 °Ccoker M3/hrAver Skin °CMax Skin °C02-H-01 Duty (Gcal/hr)
Naphtha Feed M3/hr1.000-0.120-0.0490.162-0.1960.1830.1390.1380.042
Coker %-0.1201.000-0.4630.250-0.4130.897-0.306-0.226-0.359
Heater outlet °C-0.049-0.4631.000-0.3380.605-0.4470.6150.5810.490
Heater inlet °C0.1620.250-0.3381.000-0.9230.268-0.793-0.699-0.724
Delta Temp 02-H-01 °C-0.196-0.4130.605-0.9231.000-0.4160.8490.7540.763
coker M3/hr0.1830.897-0.4470.268-0.4161.000-0.199-0.136-0.340
Aver Skin °C0.139-0.3060.615-0.7930.849-0.1991.0000.9360.717
Max Skin °C0.138-0.2260.581-0.6990.754-0.1360.9361.0000.598
02-H-01 Duty (Gcal/hr)0.042-0.3590.490-0.7240.763-0.3400.7170.5981.000

Missing values

2023-07-07T14:36:04.733043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-07T14:36:05.224896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-07T14:36:05.708457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateNaphtha Feed M3/hrCoker %Heater outlet °CHeater inlet °CDelta Temp 02-H-01 °Ccoker M3/hrAver Skin °CMax Skin °C02-H-01 Duty (Gcal/hr)
02020-01-01211.1155630.109179314.690154288.58133326.10882123.049480484.861189550.4199963.247973
12020-01-02209.1188590.110938315.062798288.88000026.18279823.199331479.636445549.1633403.055667
22020-01-03213.3919920.108509313.780351288.41600025.36435123.154847476.745390549.8826683.037318
32020-01-04209.4824270.091126315.533911286.82666828.70724419.089251491.140361559.3466643.691847
42020-01-05193.3990820.071630320.106125286.53333233.57279313.853201501.711529566.9039993.540957
52020-01-06213.1019690.089515314.939707287.08266727.85704019.075803488.361973557.4313303.244400
62020-01-07210.6247450.072832315.788236286.58666529.20157115.340294489.750444552.8639983.083802
72020-01-08212.7602590.065664314.885710286.19199828.69371313.970627493.054611559.9099933.633311
82020-01-09214.3363950.075349314.881757287.33333327.54842416.149996490.163917562.3800003.927049
92020-01-10213.8478360.098926314.764749288.51200026.25274921.155200486.197833560.2133413.320325
DateNaphtha Feed M3/hrCoker %Heater outlet °CHeater inlet °CDelta Temp 02-H-01 °Ccoker M3/hrAver Skin °CMax Skin °C02-H-01 Duty (Gcal/hr)
12362023-05-22182.8816640.028195316.640528282.41315634.2273725.156382478.865653529.3273543.731528
12372023-05-23187.9624650.024234316.008790282.11057733.8982144.555176479.187120530.7179253.940235
12382023-05-24181.5430670.030967312.892862278.57253434.3203285.621843473.714433514.8104014.024228
12392023-05-25174.4756810.038352313.096387278.83804334.2583446.691574466.872732507.6910243.787990
12402023-05-26175.4613180.040324315.426366281.10738034.3189867.075379475.565698526.6182993.811521
12412023-05-27182.1237680.037792312.774073278.01324734.7608266.882885475.998046525.1022143.779953
12422023-05-28180.4482340.038565312.598441277.52755635.0708856.958922474.871006519.2891853.807539
12432023-05-29180.4261790.037024312.192336277.65857834.5337586.680102472.698236515.0201353.799858
12442023-05-30183.6803850.031214314.508366279.24711235.2612535.733401480.587425525.0379333.964357
12452023-05-31182.7446120.029478314.298602277.04222137.2563815.386907484.107224529.2370814.136609